1. Field of Invention
This invention relates to a transformation of industrial process alarm occurrence data so that it will be suitable for standard statistical quality control analysis.
2. Discussion of Prior Art
Industrial processes are typically monitored and controlled by computers, which scan hundreds or thousands of process measurements. Associated with each measurement may be one or more alarm limits, which notify the plant operator that the plant requires special attention when the measurement exceeds the limit. A common problem in many plants is that many alarms from different measurements may occur close to one another in time, so that the operator is overwhelmed with alarms and loses effectiveness at controlling the plant. Indeed, a contributing or direct cause of many plant upsets have been due to the overwhelming number of alarms that have been presented to the operator.
This situation is of concern in industry, and there are guidelines as to the maximum amount of alarms in a given time period that an operator can handle and still be effective. In particular, the manual Alarm Systems, A Guide to Design, Management, and Procurement (EEMUA, 1999) contains numerical values of acceptable alarm metrics that have been widely adapted (but perhaps not met) by industry.
There are several software packages that will plot a statistical quality control run chart on the number of alarms per given time period, and compare these values with the EEMUA recommended values. FIG. 1 is an example of such a run chart. Often, industrial software will have the points in the run chart replaced by bars, but the two formats are essentially equivalent.
It is apparent from FIG. 1 that the number of alarms per hour is a stochastic process, and that the techniques of statistical quality control provide a suitable framework for tracking and analyzing alarm system performance. Statistical quality control techniques are widely used in industry and provide a solid theoretical framework for interpreting stochastic processes.
Suitable confidence intervals may be superimposed on the run chart, in which case it would be termed a control chart. These confidence limits give some indication whether the measurement is within acceptable limits, given the capabilities of the process, or whether attention should be given to correct the process. Statistical quality control techniques also imply a probabilistic quality to the measurement—a measurement may be outside acceptable limits due to chance alone, and not to some actual shift in the process.
There is a fundamental difficulty in interpreting the plot of alarm occurrences over time. Typically, what is found in alarm systems is that there may be some time periods where the number of alarms is considerably above the median number of alarms. Almost always, this high number is due to the occurrence of a chattering alarm. A chattering alarm occurs when the process measurement hovers close to an alarm limit, so that random movement in the process causes the alarm to go rapidly on and off as the process crosses the alarm limit. Tens or hundreds of alarms may be generated in a short period from a chattering alarm.
Interpretation of the run chart or control chart is difficult with the inclusion of the alarm chatters. It is difficult to see any trend in the data, and the plot scaling is such that the number of alarms during “normal” operation are too small to be observed on the plot.
However, several alarm events from one chattering alarm are fundamentally different than several alarm events from different alarms, as the load on the operator differs. There are 2 reasons for this:                1. The operator response for a chattering alarm is roughly the same as if the alarm had rung only once. In other words, a chattering alarm indicates that the process is at, or close to, an alarm condition, and that the operator needs to make moves to get the process back to a non-alarm condition.        2. There may be little difference in the indication of a chattering alarm as compared to a non-chattering one (a chattering alarm may have an indication that flashes on and off; a non chattering one would have an indication that stays on).        
So a sequence of chattering alarms is, as far as the operator is concerned, effectively the same as a single alarm. However, the run chart does not differentiate between a multitude of alarms from a chattering alarm and a multitude of alarms from different measurements, and therefore does not represent the true load on the operator.
From a statistical viewpoint, an assumption underlying statistical quality control charts for typical alarm data sets is not valid—the statistical distribution of the alarm counts is not normally distributed. FIG. 2 is a histogram for the data of FIG. 1, which indicates that there is a roughly normal distribution centered around 18, with another peak at 100. It is possible to develop statistical quality control charts for certain non-normal distributions (Jacobs, 1990), but these are generally of the uni-modal distributions (i.e., Gamma or Poisson); there are no suitable statistical quality control techniques for multi-modal distributions such as shown in FIG. 2.
Furthermore, a chattering alarm may be thought of as an equipment failure, and these equipment failures are not apparent from the run chart of all alarm counts. What is required is a separate run chart denoting the number of these failures versus time. This would also be a statistical quality control run chart, except that the underlying distribution would be a Poisson distribution rather than a normal distribution.
Evaluation of alarm settings was discussed in U.S. Pat. No. 6,618,691, where the purpose was to determine the best alarm setting that would give an operator adequate warning without excessive “false” alarms (i.e., alarming during normal variation). However, this technique looked at only a single alarm setting and process measurement at a time, did not consider the case of chattering alarms, and did not consider the total amount of alarms presented to the operator.
Standard Statistical Quality Control (SQC) techniques can be used to ascertain whether the process itself is in an alarm condition (see for example U.S. Pat. No. 5,257,206), but using SQC to track the number of alarms is unique to this application and is not considered in this or other references.
U.S. Pat. No. 6,308,141 pertains to analyzing the number and time of alarms on an injection molding machine to determine the state of the process. Statistical quality control is one of the methods employed, but the process was specific to injection modeling machine, and the statistical analysis of the alarms was only performed to determine characteristics of the operation, not to characterize the alarm system itself.
Statistical process control was one of the technologies employed to determine alarm thresholds in U.S. Pat. No. 7,076,695. The methods were applicable to non-normal statistical distributions, but again the purpose was to determine whether a process was in alarm, not to measure the performance of the alarm system itself.
Incorporation of statistical concepts into tracking the number of process alarms then is the crux of the problem addressed by this invention. Specifically, the use of a transformation results in a normally-distributed data set and a Poisson-distributed data set that are amenable to statistical quality control techniques.